Specifically, once the source language is translated into the target language, CASE moves the noisy representation on the target side towards a direction of space on the source side back in a form of adversarial example, and uses this as an additional training data to task classifier.

to the best of our knowledge, we are the first proposing pseudoadversarial training in latent space for transfer learning.


First, in Table 3, we can see that BiLSTM model (Conneau et al., 2018)  to perform consistently better than Translate train for all languages, which means a single English model works better than training each target model with translated data.

this refinement may have adverse effects (Yeo et al., 2018), for which a policy network P in Figure 1d decides whether to refine or not.


To demonstrate the effectiveness and flexibility of our soft-coding approaches, we evaluate TARS on five different datasets covering diverse scenarios and CASE on cross-lingual natural language inference (XNLI) datasets with 15 languages (including low-resource language such as Swahili and Urdu), and show that TARS and CASE outperform existing hard-coding approaches.

trying to keep the shared space too pure inevitably leads to sparseness, for which we additionally introduce the density constraint L dense for this purpose.


Experiments on multimodal language data show that our model achieves good results across various levels of imperfections.

our objective function is therefore a weighted combination of the prediction loss and the tensor rank regularizer in equation 4.


There has been an increased interest in multimodal language processing including multimodal dialog, question answering, sentiment analysis, and speech recognition.

recently, neural models such as cascaded residual autoencoders (Tran et al., 2017), deep adversarial learning (Cai et al., 2018), or translation-based learning (Pham et al., 2019) have also been proposed.


Furthermore, multimodal time series data exhibits correlations across time and across modalities Hidaka and Yu, 2010).

these methods often require knowing which of the entries or modalities are imperfect beforehand.


A pre-trained word2vec model (Mikolov et al., 2013) is generated to represent the word embedding W E(w)(w ∈V ).

we will follow the definition of  about top-K accuracy.


Our embeddings also contain different granularity levels of embedding, but the word vocabulary is capable of being updated in accordance with users' inputting choice during IME working.

the performance on touch-Pal corpus is similar and even more obvious; our best setting achieves 14.35% improvements compared to the best baseline.


As the core of Chinese IME, P2C conversion has been formulized into a seq2seq model as machine translation between pinyin and character sequences, there are still a few differences between P2C converting and standard machine translation.

in practice, we need to control the size of the vocabulary for acceptable decoding speed.


(2017), which achieved a log likelihood of −21.73, suggesting that neural networks may be just as good at capturing phonotactic regularities as models that generate upcoming phonetic segments via stochastic memoization.

comparing them, we see that the feature-aware model maintains manner-based distinctions even at late stages of the clustering.


For models trained on the disjoint supervision setting, we use the character vocabulary provided for each language in the MWC dataset (see Kawakami et al.

since morphological features are annotated at the word-level, we convert these annotations to the character level by placing each annotated word's tags as supervision on the first character (which we found to outperform supervising the last character in preliminary results).


These attributes suggest that CLMs can model regularities that exist within words, such as morphological inflection.

for the UD language models, we train models with two hidden layers for 150 epochs with a batch size of 10.


We use the mean reward over the samples as our baseline reward.

we directly minimize a distance function between strings, e.g., Levenshtein distance, by using negative distance as a delayed reward: 1 R(Ŷ ) = −Levenshtein(Y,Ŷ ).


We note that debiasing methods can be categorized as retraining and inference (see Table 3).

there is a long history of gender stereotype study in law, psychology, media study, and many other disciplines which we do not discuss.


Automatic speech detection works better with male voices than female voices (Tatman, 2017).

this allows for greater flexibility; the gender dimensions can be used or neglected.


(2018) propose a variation on the traditional generative adversarial network (Goodfellow et al., 2014) by having the generator learn with respect to a protected gender attribute.

we name these data sets Gender Bias Evaluation Testsets (GBETs).


We consider the squared inner-product between v g and the debiased stereotypical or gender-neutral words as the loss, L g , as given by (4).

by applying the proposed method, we can further remove any residual gender biases from GN-GloVe, which shows that the proposed method can be applied in conjunction with GN-GloVe.


Prior work on gender debiasing (Bolukbasi et al., 2016;Zhao et al., 2018b) showed that the vector difference between the embeddings for malefemale word-pairs such as he and she accurately represents the gender direction.

such a re-annotation exercise would be costly and inconsistent with the original ratings.


We are interested in evaluating whether this "corrects" the profession bias by mixing signals, e.g., while "doc-tor" biases towards a male translation, "pretty" tugs the translation towards a female inflection.

on the one hand, this allows for a controlled experiment environment, while, on the other hand, this might introduce some artificial biases in our data and evaluation.


Recently, the much celebrated Long-Short Term Memory (LSTM) neural network model has emerged as a successful model to learn representations of sequences, thus providing an ideal solution for many Natural Language Processing tasks whose input is sequence-based, e.g., sentences and phrases (Hill et al., 2016;Melamud et al., 2016;Peters et al., 2018).

in marked contrast to the other LSTM-based approaches in the literature, we use sensetagged text to provide input contexts of the kind s i−W , .


Where S is a non-empty set of ordered word pairs (x, y) that define the association, b is the first principal component of { x − y | (x, y) ∈ S}.

(2016) suggested, word embeddings should not be normalized before debiasing, as vector length can contain important information (Ethayarajh et al., 2018).


λ ← 1 because we expect Figure 1: Before debiasing words using subspace projection, one needs to identify which words are genderappropriate -to avoid debiasing them.

similarly, α ← −1 because it minimizes the difference between x − y and its information theoretic interpretation over the gender-defining word pairs in s, though this is an estimate and may differ from the true value of α.


Word embeddings are often criticized for capturing undesirable word associations such as gender stereotypes.

if only a single word pair (x, y) defines the association, then the relation vector b = ( x − y)/ x − y , making RiPA highly interpretable.


We also prove that WEAT, the most common association test for word embeddings, systematically overestimates bias.

this is not guaranteed to hold, especially for more nebulous attribute sets (e.g., 'pleasant' vs. 'unpleasant' words).


We provide a full list of the lemmatized, gendered, animate nouns in App.

consider the following restricted version of our model.


We represent n via a multi-hot vector f n ∈ {0, 1} T of its lexical features-i.e., its genderless lemma, its gender (male or female), and its number (singular or plural).

let η (•) : V → R 2 be the maximum-likelihood estimate for the special case of our model without (latent) sentiments: Then, we have Proof.


Our study has a few limitations that we wish to highlight.

we provide frequencies for the senses where, after Bonferroni correction, the differences between men and women are significant.


The large size of D S is not a problem: we use an inverted index that we probe with documents from D T to efficiently identify D S .

yet another option is to learn projections to align generic embeddings to the target sense (Bollegala et al., 2015;Barnes et al., 2018;K Sarma et al., 2018), or to a shared common space (yin and Schütze, 2016;Coates and Bollegala, 2018;Bollegala and Bao, 2018) in carefully controlled experiments, none of the proposed approaches to adapting pretrained embeddings consistently beats the trivial baseline of discarding them and training afresh on D T !


(2017) present the model Attract-Repel to improve qualities of word embeddings for synonymy recognition.

instead of learning individual term embeddings, we directly map all the relation triples to the hyperspherical embedding space such that different types of lexical relations have diverse embeddings in terms of angles.


Completing the graph, we establish 9 candidate plural-singular pairs.

details concerning the rules used to construct the cases can be found in the Appendix.


These transformations are discovered through the use of an unsupervised character aligner.

their method is dependent upon an English corpus tagged with more morphologically aware tags than are typically produced by an off-the-shelf English POS tagger.


Figure 4: Forming a consensus from morphologicallyinformed languages.

lemmas are only one of many available English annotations that may be able to benefit LRLs.


This result indicates that the claiming of the deep network defeating Transformer-Big is established and is not affected by the size of the data set.

then BPE with 32k operations is used for both sides independently, resulting in a 44k Chinese vocabulary and a 33k English vocabulary respectively.


In this way, we can see that both post-norm and pre-norm are special cases of the general residual unit.

it is still an open question for the discipline.


The cluster ids are then used as sentence codes.

the translation results sampled with the syntactic coding model have drastically different grammars.


We focus on data representation, an adequate function for the selection process, and studying how to avoid additional hyperparameters that depend on the input corpus.

in the selection process that follows, we consider four strategies.


We show that a naïve method to create compressed phoneme-like speech representations is far more effective and efficient for translation than traditional frame-level speech features.

to reduce temporal resolution, but rather use network-in-network (NiN) projections from previous work in sequence-to-sequence ASR (Zhang et al., 2017; to get the same total 4× downsampling in time.


We extract 40-dimensional Mel filterbank features with per-speaker mean and variance normalization using Kaldi (Povey et al., 2011).

for Mboshi, we use a method that does not require language-specific data to generate a phoneme-like sequence.


Finally, VG-NSL can be easily extended to multiple languages, which we evaluate on the Multi30K data set (Elliott et al., 2016(Elliott et al., , 2017 consisting of German and French image captions.

in order to keep consistent with VG-NSL, we set τ = 20 in all of our experiments.


2011, which produces dependency parse trees based on automatically induced pseudo tags.

next, it matches textual representations with visual inputs, such as the paired image with the constituents.


We thank the reviewers for their helpful comments and Nham Le for helping with the initial data collection.

our method provides a direct way to model visual relationships in language.


Some examples from the VID-sentence dataset are shown in Fig.

it is desirable to have a diverse distribution of the matching behaviors n=1 , we propose a diversity loss L div to strengthen the matching behaviors between reliable instance-sentence pairs and penalize the unreliable ones during training.


A special role here takes definite determiner 'the', which, in spite of the stark decline of determiners in general, increases by 13% in absolute occurrence counts between the first and last round of a game, suggesting a shift towards known information.

each image can be assessed independently using a decision threshold (set to 0.5).


To learn a cell architecture on a task, we provide the performance of the sampled cell structure on the validation set of the given task as reward to the controller.

similarly, we show that the cell structure learned from jointly training on the MsR-VTT and MsVD video captioning datasets performs better on the DiDeMo dataset than the individually-learned cell structures.


Initially, given the dataset d 1 , we train the architecture search model to find the best Directed Acyclic Graph (DAG) structure for RNN cell and model parameters θ 1,k under the block sparse condition described above in Sec.

our aim is to find a generalizable cell structure which jointly performs well across different tasks/datasets {d 1 , d 2 , .., d n }.


Modern entity linking systems rely on large collections of documents specifically annotated for the task (e.g., AIDA CoNLL).

our model outperformed all of them on all test sets.


This result may be interpreted as suggesting that humanannotated data is not beneficial for entity linking, given that we have Wikipedia and web links.

recall that every mention in these documents has been manually annotated or validated by a human expert.


We start with a set of candidates for a mention m containing all entities refereed to with anchor text m in Wikipedia.

we believe that the two sources of information are likely to be complementary.


Misra et al., 2018;Chaplot et al., 2018;Mei et al., 2016).

this operation is analogous to a softening of a sample from the distribution.


Supervised models suffer from the problem of domain shifting where distribution mismatch in the data across domains greatly affect model performance.

the number of bags, N , is set separately for each dataset to ensure a uniform bag size of 1K samples.


We observe around 50 epochs of training to reach convergence of our approach.

to learn the SDG, each Φ B j is measured with Φ t to give a reward in our framework, which is described in the following subsection.


Note that, different from aforementioned measurements, LOSS requires labels from the target domain, thus is only set as a comparison to other measurements used in our approach.

their selection becomes a consuming process where the complexity is determined by the amount of the source data.


Results POS tagging results are reported in Table 2.

such pre-designed metrics are, always, neither able to cover effective characteristics for transferring domain knowledge nor can be applied in different data nature.


Extensive experiments results have demonstrated the effectiveness of the proposed model.

we constructed extensive experiments on three real-world review datasets.


Generator For a particular word w, it may occur multiple times in the reference title or in multiple related entities.

our ambitious goal is to speed up scientific discovery and production by building a PaperRobot, who addresses three main tasks as follows.


In fact, 11% sentences in human written abstracts include repeated entities, which may mislead the language model.

it has been shown to be associated with poor prognosis.


Following previous work (Yi et al., 2018b), we consider four criteria for human evaluation: • Fluency: Whether the generated poem is grammatically correct and fluent.

the use of metaphor and personification, both frequently used forms of rhetoric, are able to enrich the emotional impact of a poem.


To balance linguistic accordance and aesthetic innovation,  adopt memory network to choose each term from reserved inventories.

encoder: Here we implement the encoder as an LSTM (Hochreiter and Schmidhuber, 1997) model, which aims to integrate topic information.


With these additional background information, our model is able to make full expan-sion to generate the novel and coherent essay.

it is unreasonable to only use BLEU for evaluation because TEG is an extremely flexible task.


Intuitively, if the model performs well in content preservation, it is easy to back-reconstruct the source input x.

each item contains the source input, the sampled target sentiment intensity value, and the output of different systems.


Jason Isringhausen (0-0) allowed 0 runs, 1 hits and 0 walks in 1 innings.

we report experiments on the benchmark RO-TOwIRE dataset (wiseman et al., 2017) which contains statistics of NBA basketball games paired with human-written summaries.


There are no existing head-modifier templates to train from, therefore we use the dependency parsing technique (Manning et al., 2014) to acquire templates in training data.

and the results demonstrate that our head-modifier template plays an important role in generating type descriptions.


In this task, the output may still be correct judging by input infobox even if it's different from the ground truth.

aside from such syntax templates, in the future, we aim to explore how semantic templates contribute to type description generation.


In order to analyze the effect of pseudo parallel data, we conduct ablation study by adding the data to the baseline models and removing them from our models.

figure 2 shows the performance of a table-to-text model trained with different number of parallel data under the encoder-decoder framework.


Supervised systems like PBSMT, as expected, simplify the sentences to the maximum extent.

in the adversarial phase, adversarial and diversification losses are introduced alongside denoising and reconstruction losses for fine-tuning the encoder and decoders.


The variational posteriors q φ (z x |x) and q φ (z y |y) follow Gaussian distributions parameterized by the outputs of feedforward networks, whose inputs are x and y.

the Kullback-Leibler (KL) divergence between q φ (z x |x) and the isotropic Gaussian prior So we only need to model µ x and the diagonal of Σ x to compute the KL divergence.


Experimental results of language modeling are evaluated by the reconstruction loss using perplexity and the targeted syntactic evaluation proposed in (Marvin and Linzen, 2018).

unsupervised paraphrasing can be performed using both SIVAE-c and SIVAE-i.


most of these syntax-aware generation works only focus on neural machine translation.


Figure 3: Example questions generated by human (i.e., original questions denoted as OQ), NQG and our ReDR on CoQA.

coQA contains text passages from diverse domains, conversational questions and answers developed for each passage, as well as rationales (i.e., text spans extracted from given passages) to support answers.


The symbol "+" stands for the corresponding module is added on the "Basic model" which is a vanilla Transformer with 4 identical layers.

the decoder in our framework has a similar stacked structure with N identical layers.


Then, a similarity matrix S ∈ R m×n is calculated for a given article representation, S ∈ R m×d , and a template representation, T ∈ R n×d : where f is the similarity function, and the common options for f include: Most previous work uses dot product or bilinear function (Chen et al., 2016) for the similarity, yet we find the family of Euclidean distance perform much better for our task.

to examine the effect of this module, we studied its ranking quality under different ranges as in Section 4.1.


• Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says.

many aspects of this approach are largely underinvestigated, such as determining the set of source sentences to be fused, handling its large cardinality, Sentence Pair: Merged Sentence: (A) The bombing killed 58 people.


To narrow down the focus into topically-relevant segments, we jointly model topic segmentation and summarization.

p i ∈ P is the the speaker of utterance u i , where P denotes the set of participants.


Moreover, these methods tend to have limited content coverage by selecting salient words.

each meeting is divided to six to ten segments, with special focuses on topics such as 'openings', 'trend watching', 'project budget' and 'user target group'.


Artificial Titles enable the decoder to be trained on these tokens.

the mixed performance of training on t art indicates the artificial title quality is lower for StackEx, (d) vs. (b).


In the third section, the labeled source is mixed with target domain artificial titles and trained using (Pryzant et al., 2017)'s Discriminative Mixed (DM) and Adversarial Discriminative Mixed (ADM) machine translation models.

the ASADA (f) embedding is closer to the T art embedding and more compact, as is T art .


6 to the validation sample, the fraction of incorrect summaries increases from 26% to 29% (Table 2), demonstrating that the slight improvement on the validation data does not transfer to the test set.


Three medical experts performed the manual summarization of the 1K questions using the following guidelines: (i) the summary must allow retrieving correct and complete answers to the original question and (ii) the summary cannot be shortened further without failing to comply with the first condition.

treating other problems that may cause pseudogout may make the condition less severe" which I would like to have explained, especially what those other problems are &how they may be treated.


Empirical studies have shown that our approach achieves comparable results upon automatic evaluation and improves the grammaticality of compression based on human evaluation.

multi-sentence compression (mSC) aims to generate a single shorter and grammatical sentence that preserves important information from a group of related sentences.


Section 4 reviews related work and Section 5 concludes this paper with future works.

we first encode the input text into distributed vector representations by taking account the context.


In this work, we adopt 1D convolution as the local encoder.

some papers propose variants of self-attention mechanisms, such as shen et al.


subsets of the training examples) so as to study the relationship between the proportion of the available training examples and the performance.

biLSTM) and pre-trained word vectors (e.g.


Moreover, our approach surpasses the pipelined baseline with faster inference speed on both TriviaQA-Wikipedia and SQuAD-document.


Inspired by the non-maximum suppression (NMS) algorithm (Rosenfeld and Thurston, 1971) that is used to prune redundant bounding boxes in object detection (Ren et al., 2015), we present a span-level NMS (Algorithm 1) to alleviate the problem.

removing both the retriever and the reranker performs the worst, which only achieves 68.1/81.0 F1 on two datasets at peak.


As a result, "Macau" is chosen as the final answer.

the above labels are annotated for each candidate in B, yielding y hard 2 R M ⇤ and y sof t 2 R M ⇤ .


The input to our model is a question q and a set of documents Since the documents could be retrieved by a search engine (e.g., up to 50 webpages in the unfiltered version of TriviaQA (Joshi et al., 2017)) or Wikipedia articles could contain hundreds of paragraphs, we therefore first discard irrelevant document content at paragraph level.

the top-4 candidates contain very similar reading scores from 11.1 to 11.3, which matches the observations of Clark and Gardner (2018).


In the classic and most simple form of QA, questions are formulated in such a way that the evidence required to answer them may be contained in a single paragraph, or even in a single sentence.

in each epoch, a question appears as a training sample four times; once with the relevant paragraph, and three times with randomly sampled irrelevant paragraphs.


are duplicated many times in the dataset; we downsample such questions to a maximum limit of 10.

we then condition a neural question generation system on these two classes, which enables us to generate both types of questions from a paragraph.


At the end of the film, it was revealed that Yoda has been in contact with Qui-Gon's spirit, learning the secret of immortality from him and passing it on to Obi-Wan.

we use beam search with a beam size of 3 to generate three highly-probable question candidates.


We consider that the sequential extraction and the adaptive termination help to prevent overextraction.

query Vectors Figure 3: Overview of query Focused Extractor at step t. z t is the current summarization vector.


On SQuAD1.1 where the best models already outperform humans, questions remaining unsolved are really difficult ones.

given passage P = {p i } m i=1 and question Q = {q j } n j=1 , we first pack them into a single sequence of length m + n + 3, i.e., where SEP is the token separating Q and P , and CLS the token for classification (will not be used in this paper).


Very recently, Lample and Conneau (2019) proposed a multilin-gual language model that leveraged both monolingual and parallel data.

we collect questions from this session, and use the entity name as well as its aliases from wiki-Data 2 knowledge base as golden answers.


There is a large gap between the performance of English and that of other target languages, which implies that the task of cross-lingual OpenQA is difficult.

many of the words in translate-train German data do not have pretrained word vectors.


To investigate this, we use the parsed dataset to obtain pairs of the form is the j-th subtree of the (approximate) MAP tree t (n) for the n-th sentence.

tween the original model and the URNNG's structured inference network, which is parameterized as a neural CRF constituency parser (Durrett and Klein, 2015;Liu et al., 2018).


The multi-source domain adaptation problem assumes there are labeled datasets for multiple source domains.

since ELMo uses charLsTM to learn the first-layer word representations, we did try to expand the character dictionary with those that only occur in the target-domain unlabeled data, and randomly initialize their corresponding char embeddings before fine-tuning ELMo.


In this work, we choose two typical domain-aware web texts for annotation, i.e., product blogs and web fictions.

this only produces slight and inconsistent performance gains.


With the dictionary, we know that "Joe" is an entity word.

in ordinary supervised learning, we estimate R with the empirical lossR : and update model parameters to learn a classifier f * that minimizesR : Unbiased positive-unlabeled learning (uPU) (du Plessis et al., 2014) aims to estimate R when there are only a set of positive (P) examples and a set of unlabeled (U) examples, which contains both positive and negative examples.


It should be noted that some results on NER are not comparable to ours directly, as their final models are trained on both training and development data 4 .

the shallow connections between consecutive hidden states of RNNs and insufficient modeling of global information restrict the potential performance of those models.


Linguistically motivated similarity penalty and categorical distance constraints are also imposed on the inducer as regularization.

current PCFG induction models, using word tokens as input, are unable to incorporate semantics and morphology into induction, and may encounter issues of sparse vocabulary when facing morphologically rich languages.


(2002) for CCG than to the more standard practice of evaluating parsers in terms of just local dependencies (e.g.

while these parsers model the phrase structure and locality constraints of TG, they are not transformational: movement is merely 'simulat [ed]' (Lin, 1993, page 116) by passing features up a precompiled network of nodes representing a tree, from the site of the trace to the site of the antecedent, with the latter merged directly into its surface position, in the style of GPSG.


The model with only text modality fails to detect sarcasm as for example (c), though the word so is repeated several times in example (c).

the Concat(3) model outperforms Concat(2), because adding attributes as a new modality actually introduces external semantic information of images and helps the model when it fails to extract valid image features.


It has proven useful for citation recommendation (Bai et al., 2018) and conversation understanding (Zeng et al., 2019).

the comparison results with all neural-based models are shown in Figure 2.


7 For the target keyphrases, we employ userannotated hashtags for Twitter and Weibo following Zhang et al.

we adopt a soft switcher λ j ∈ [0, 1] to determine whether to copy a word from source as the j-th target word: with W λ and b λ being learnable parameters.


We repeat the same procedure after removing s max from S, and continue until S reduces to an empty set.

an analogue multi-task formulation can also be used for the Margin Ranking loss as: We use a combination of corpus-based and linguistic features to rank the segmentations.


In this paper, we reimplemented 2 and tailored this approach to segmenting hashtags by using a language model specifically trained on Twitter data (implementation details in §3.6).

after reexamination, we found annotation errors in 6.8% 5 of the hashtags in this dataset, which is significant given that the error rate of the state-of-theart models is only around 10%.


We can see this dynamic reflected in the Joker's profile score, as a high-powered, high-agency, low-sentiment character, who is the primary plotdriver.

our analysis focuses on a different data set and coverage type-we examine general articles rather than post-match interviews.


The ELMo embeddings and unmasked BERT embeddings perform approximately the same.

for example, many low-agency nouns tend to be inanimate objects (i.e.


To the best of our knowledge, this is the first work to target affective dimensions in pre-trained contextualized word embeddings.

whereas the output of a persona model is distributions over personas and vocabulary, our system outputs scores along known dimensions of power, agency, and sentiment, which are easy to interpret and visualize.


The unmasked BERT embeddings also achieve positively correlated scores, though these correlations are not statistically significant.

m is a d × 2N dimensional matrix, where d is the dimension of the embeddings.


http://www.pewinternet.org/2018/03/ 01/social-media-use-in-2018/ claims being produced on daily basis.

the attention vector h c S is the high-level representation of the claim with the embedded evidence based on the hierarchical attention method.


s5: [Entail]: The radiation created on the attempt of 5G cellular networks are not harmful only for birds but also for humans too.

some claim verification tasks such as FEVER (Thorne et al., 2018a) are particularly defined to classify if the factual evidence from the source like Wikipedia, which rarely remark on the veracity of the mutated claim, can infer the claim as being supported, refuted or NEI.


We represent a history as a sequence of documents, D, written by the user, that contain information about the kinds of activities that they have done.

several studies have applied computational approaches to the understanding and modeling of human behavior at scale (Yin et al., 2014) and in real time (Wang et al., 2015).


Using such a random sample would require us to filter out a large percentage of the total data collected, making the data collection process inefficient.

during each train- 2.00 4.00 6.00 10.00 20.00 50.00 50.00 Table 10: Per-class accuracy (%) @ k eval and ACR scores for the 50-class prediction task.


Given the relative novelty of the task, we consider model performance as secondary to the broader scientific goal of identifying which features are determinants of variation as a function of socio-economic group.

price range as proxy To annotate the Yelp!


Inspired by Labov's seminal work on stylistic variation as a function of social stratification, we develop and compare neural models that predict a person's presumed socioeconomic status, obtained through distant supervision, from their writing style on social media.

we hypothesise that some of our target classes are typified by certain writing styles which differ in their formality and the extent to which they approach informal speech.


The work presented here takes inspiration from this paradigm, and contributes to the growing literature on distant supervision in NLP (Read, 2005), especially in social media (e.g.

syntactic information is almost as predictive and is a much better signal for stylistic varia-tion.


The graph vertices are connected via a set of edges described hierarchically, as follows: • E U P ⊂ E: All the Twitter users are connected to the political users whom they follow.

the sizes of hidden states for both GCN layers are 16.


It can be regarded as special cases of a simple differentiable message-passing framework (Gilmer et al., 2017): where h is the hidden state of node v i in the l-th layer of the neural network, with d (l) as the dimensionality of representation at layer l. N (i) is the set of direct neighbors of node v i (usually also include itself).

since the text and social representation use the same space, user bias information can be propagated to the document representation, acting as a distant supervision source.


For the second evaluation, we create a benchmark distribution by randomly sampling sentences within reviews and averaging the length of each span formed by those sentences.

as it is a balanced dataset (52.72% of the sentences are spoilers), we set η = 1.


This is complementary to the self-reporting approach, but may have scalability issues as it is relatively difficult to engage sufficient consumers in a timely fashion.

this is complementary to the self-reporting approach, but may have scalability issues as it is relatively difficult to engage sufficient consumers in a timely fashion.


Analyzing comments on online news services or discussion forums has been extensively studied (Wanas et al., 2008;Ma et al., 2012;Brand and Van Der Merwe, 2014;Llewellyn et al., 2016;Shi and Lam, 2018).

in this paper, we address directly evaluating the quality of comments on the basis of "constructiveness," separately from user feedback.


2 Dataset Creation 2.1 Definition for "Constructiveness" According to the dictionary, 4 "constructive" means "having or intended to have a useful or beneficial purpose."

-Main-condition 3: The comment gives a new idea, solution, or insight.


-Main-condition 4: The comment is a user's rare experience related to the article.

their approaches were based on user feedback, which is completely different from constructiveness, as explained in Section 2.


For example, other environmental factors like humidity and temperature can affect the air quality when real-world events like wildfires may also play a role.

in general cases, air quality changes gradually most time, which may be not sufficiently documented in social media.


For example, 81% of the adults in the USA spend on average two hours on social media and collectively publish 170 million tweets 1 every day on their feelings and observations (Wu et al., 2018).

in recent centuries, industrialization has considerably changed human society by providing a stimulus to economic growth and improved life quality.


For all experiments, we use the pre-trained GloVe word embedding (Pennington et al., 2014) with a dimension of 50.

to evaluate the effectiveness of our model, we compare DAML with the following two baselines: • ZSDG (Zhao and Eskénazi, 2018) is the state-of-the-art dialog domain adaptation model.


Qualitatively, the model can often outline clear action sequences, as shown in Figure 7.

this result highlights a key advantage of multistage modelling: the usage of specialized modelling techniques for each sub-task.


First, we remove the context-sensitive transformation TRANS(•) and then find significant performance degradation.

the WM consists of a Short-term Storage system (StS) and a Central-EXE including an Attention Controller (Attn-Ctrl) and a rule-based word selection strategy.


As revealed by adversarial tests (Jia and Liang, 2017), models for single paragraph QA tend to seek answers in sentences matched by the question, which does not involve complex reasoning.

our framework, however, gradually discovers relevant entities following clues.


Meanwhile, our ensemble model ranks 1st place on the WIKIHOP leadrboard and surpasses the human performance (as reported in (Welbl et al., 2018)) on this data set by 0.2% 1 .

to keep the dimensionality consistent, we apply a single-layer multi-layer perceptron (MLP) with tanh activation function to increase the dimension of the query subject entities to 2h.


Out of vocabulary words are initialized with random vectors.

removing one type of nodes means the nodes and corresponding edges do not exist in the HDE graph.


At every step, the DE selects a document which is related to the current memory state and updates the internal memory.

we adopt an LSTM-RNN with an attention mechanism (Bahdanau et al., 2015) to encode theĥ T to ancestor-aware representations y by attending to [ĥ 1,...,T −1 ].


We call this pattern of bypassing the first reasoning hop the "reasoning shortcut", and we find such shortcuts exist frequently in the non-comparison-type examples in HotpotQA.

here, with the output distribution from the control unit, q c represents the context information that is most relevant to the sub-question of the current reasoning hop, as opposed to encoding the context most related to any question word in the original bi-attention.


Following, the normalized path scores are summed for each candidate to give a probability distribution over the candidate answer choices.

section 4) connecting h e to each c k from the supporting passages P. The j-th 2-hop path for candidate c k is denoted p kj , where p kj = h e → e 1 → c k , and e 1 is referred to as the intermediate entity.


Notably, our path extraction method can be easily extended for more hops.

we compare with the Knowledge Enhanced Reader (KER) model (Mihaylov et al., 2018).


A strength of these models is high interpretability, arising from explicit pathbased reasoning over the underlying graph structure.

the proposed approach can be applied to m-hop reasoning, as discussed briefly in the corresponding sections for path extraction, encoding, and scoring.


The resulting extracted paths can be summarized as a set of entity sequences.

our qualitative analysis on a sampled set of instances from WikiHop development set shows that the top scoring paths in 78% of the correctly answered questions have implied relations in the text that could be composed to derive the query relations.


Automatic metrics allow faster measures of progress when training and testing models and easier development of text generation systems.

we introduce two new metrics: sentence mover's similarity (SMS), which relies only on sentence embeddings, and sentence and word mover's similarity (S+wMS), which uses word and sentence embeddings, as in Figure 1.


For all model adjustment experiments, we use the architecture of our suggestion models described in Section 4.1.

in this section, we examine the effects of annotation suggestions in detail, considering interannotator agreement, intra-annotator consistency, annotation bias and speed, as well as usefulness of suggestions and the impact of universal versus personalised suggestion models.


We train ten models with different seeds for each setup (universal and three personalised for MeD and TeD), applying the same parameters for all of them: one hidden layer of 100 units, variational dropout rates for input and hidden layer of 0.25, and the nadam optimiser (Dozat, 2016).

we also investigate continuous updates of the suggestion model during the annotation process, but focus on a task in which annotators require vast training and domain expertise.


In contrast, bundle sizes of 30 and higher show a steady increase in performance, similar to CUM.

(2018) created annotated corpora of epistemic activities for 650 texts in the medicine domain (MeD) and 550 in the school teaching domain (TeD).


(2018) proved that without further assumptions, H 0 will be rejected with a significance level of α if: where F n , G m are the empirical CDFs with n and m samples, respectively, is the violation level, Φ −1 is the inverse CDF of a normal distribution andσ n,m is the estimated variance of the value where is computed using samples X * n , Y * m from the empirical distributions F n and G m .

we consider this ambiguity an inherent limitation of the COS method.


However, according to this relaxation, X ∼ F is indeed stochastically larger than Y ∼ G. Almost Stochastic Dominance To overcome the limitations of the above straight forward approach, and define a relaxation of stochastic order, we turn to a definition that is based on the proportion of points in the domain of the participating distributions for which SO holds.

we compute ε such that a value of 0 would mean that the noisy version is superior.


Existing datasets for scoring text pairs in terms of semantic similarity contain instances whose resolution differs according to the degree of difficulty.

2017, we assign a positive label if relatedness ≥ 4 and a negative one otherwise to use a similar criterion as in the other datasets.


It indicates the sparse user activity in conversations, where most users engage in a conversation only once or twice.


secularism allows every citizen to freely profess his faith.


Frequent combinations at the top level are C2+C8 (procedural aspects of residency) and C1+C5 (international perspective on migration control).

all embeddings are fed to a bidirectional LSTM layer for contextualization.


In this study, we challenge this assumption and study the linguistic differences between posts signed by the account owner or attributed to their staff.

we analyze the linguistic topics and style features that distinguish the two types of tweets.


Past research has experimented with predicting user types or traits from tweets (Pennacchiotti and Popescu, 2011;McCorriston et al., 2015).

the rest of the tweets are implicitly attributed to their staff.


posts are rarely authored by the public figure who owns the account; rather, they are posted by staff who update followers on the thoughts, stances and activities of the owner.


(2008) notice a lack of consistence in how sarcasm is defined by people of different socio-cultural backgrounds.

for all tweets in Riloff, we checked the agreement between containing such a tag, and being manually annotated as sarcastic.


Event extraction has been treated as a supervised or semi-supervised (Liao and Grishman, 2010a;Huang and Riloff, 2012) task.

we investigate three incrementally more complex neural latent variable models for ODEE.


(3), local matching was conducted between the query instance and each support instance separately to get their vector representations {( s k , q k ); k = 1, ..., K} (model 7).

the final class prototype for each support set is obtained by attentive aggregation over the representations of its support instances, where the weights are calculated using the query instance.


In this section, we use similarity as an adaptive margin in softmax-margin loss to improve the performance of relation extraction models.

we show the statistics of the dataset we use in Table 3, and the construction procedures will be introduced in this section.


We introduce a conceptually simple and effective method to quantify the similarity between relations in knowledge bases.

all these split relations are regarded as different ones during training, and then different relation similarity metrics are adopted to merge those subrelations into one relation.


A Proofs to theorems in the paper If we have a proposal distribution q(x) satisfying ∀x, f (x) = 1 ∧f (x) = 1 ⇒ q(x) = 0, then equation (16) can be further written as Sometimes, it's hard for us to compute normalized probability q.

1Sometimes relations are also named properties.


During testing, for each entity pair (h, t), TransE rank relations according to d(h + r, t).

corrupted triples with similar relations can be used as highquality negative samples.


This further supports our argument that training by matching the blanks can significantly reduce the amount of human input required to create relation extractors, and populate a knowledge base.

in this task, examples in the test and development sets typically contain relation types not present in the training set.


Efforts have been made to build general purpose extractors that represent relations with their surface forms, or which jointly embed surface forms with relations from an existing knowledge graph.

for all four tasks, the model using the ENTITY MARKERS input representation and ENTITY START output representation achieves the best scores.


We run a hyperparameter grid-search over 4-fold CV with C: (0.1, 1, 10), and gamma: (0.001, 0.01, 0.1, 1).

duration model The temporal duration representation g dur k for the event referred to by the k th predicate is defined similarly to the event representation, but instead of stacking the predicate's span, we stack the hidden representations of the entire sentence H. where A SENT dUR 2 R d⇥size(g pred k ) and b SENT dUR 2 R d .


This criticism motivates our chosen algorithm TTTS (summarised in Algorithm 2), where instead of sampling a single model according to π, we sample two distinct models.

in addition, this approach extends to the asynchronous setting, where rather than waiting for the whole batch of B models to be evaluated before choosing the next batch, each worker can draw a new model to evaluate according to the updated π.


In this work, we focus on whether the attention weights' suggested importance ranking of I faithfully describes why the model produced its output, echoing existing work on explanation brittleness for other model components (Ghorbani et al., 2017).

one point worth noting is the facet of interpretability that our tests are designed to capture.


In Figure 11, we provide the distributions of the original attention probability distributions that were zeroed at the point when different ranking schemes achieved their first decision flips.

the idea of a minimal set of inputs necessary to uphold a decision is not new;  use reinforcement learning to attempt to construct such a minimal set of words, Lei et al.


Embeddings For the BERT embeddings we use the vector associated with the first token (CLS) for a given layer.

for each representation type we compute a matrix of similarities between pairs of stimuli.


rolfe (2017) learns a relaxation of a discrete random variable based on a tractable mixture of a point mass at zero and a continuous reparameterizable density, thus enabling reparameterized sampling from the half-closed interval [0, ∞).


SVM (Lei et al., 2016) 0.0154 BiLSTM (Lei et al., 2016) 0.0094 BiRCNN (Lei et al., 2016)  Deterministic predictions.

in this paper we focus on binary outcomes rather than K-valued ones.


RECL is defined on a model group instead of a single model, and the gain of a long context is measure by the relative improvement over the best short context model.

for completeness, we summarize the computational procedure for a N -layer Transformer-XL with a single attention head here.


We also show the percentage of source words or subwords in the training corpus of five domains being covered by the WMT14 corpus.

to remedy this problem, we propose an unsupervised adaptation method which finetunes a pre-trained out-of-domain NMt model using a pseudo-in-domain corpus.


It has been previously noted that neural machine translation (NMT) is very sensitive to domain shift.

by combining both bT and DALI, the output becomes fluent and also contains correctly translated in-domain keywords of the sentence.


On the other hand, data-based methods perform adaptation either by combining in-domain and out-of-domain parallel corpora for supervised adaptation (Luong and Manning, 2015;Freitag and Al-Onaizan, 2016) or by generating pseudo-parallel corpora from indomain monolingual data for unsupervised adaptation (Sennrich et al., 2016a;Currey et al., 2017).

to this end, we compare DALI and Bt on adapting from subtitles to medical domains, where the two largest corpus in subtitles and medical domains have 13.9 and 1.3 million sentences.


Instead of naively mixing out-of-domain and in-domain data, Britz et al.

we scan the source and reference of the test set to count the number of valid hits C, then scan the output file to get the count C t in the same way.


The bilingual model is proposed to improve NMT by providing a golden translations according to rest samples in the training corpora.

b-RefNet learns this mapping with a supervised regression function derived from LCC.


Translation given by NMT is not readable.

second, the global contexts in this work is static.


But in simultaneous translation, we need to translate concurrently with the (growing) source sentence, so we design a new prefix-to-prefix architecture to (be trained to) predict using a source prefix.

as a very simple example within the prefix-toprefix framework, we present a wait-k policy, which first wait k source words, and then translates concurrently with the rest of source sentence, i.e., the output is always k words behind the input.


(2017), might be seen as special cases in this framework, we note that only their decoders are prefix-to-prefix, while their training is still fullsentence-based.

in this example, due to the required long-distance reordering between English and Chinese (the sentence-final English clause has to be placed very early in Chinese), any wait-k model would not work, and a good policy should wait till the very end.


the approximate number of training pairs in EN-DE and EN-FR datasets are 4.5M and 36M respectively; newstest2013 and newstest2014 are used as the dev and test sets.


One special kind of noise is the homophone noise, where words are replaced by other words with similar pronunciations.

in Table 4, we report the performance of baseline model and our model with β = 0.95, with and without data augmentation.


When concatenating human post-edited data and artificial triplets, these systems greatly improve the MT baseline.


Since the NMT system does very few errors on this domain this data weighting is important for the APE model to learn to do fewer corrections to the mt output.

unfortunately, human post-edits are typically scarce.


Adding the artificial eSCAPE dataset  leads to a performance of 17.15 TER, a new state of the art.

this is only used in one of the pre-training phases of the language model (translation language modelling) and not in the downstream task.


3 Our model's implementation is publicly available.

a language generation task such as APE presents additional challenges.


Then we combine and shuffle all the Translationese/target parallel data and train the model on the result.

a naive translation of each source token to its top translation option without considering the context is not the best way to go.


We compare the two methods we propose, train-by-appending and train-by-replace with the constrained decoding algorithm of  available in Sockeye in identical conditions and using a beam size of 5.

trainby-append still performs better than the baseline, while constrained decoding shows significant BLEU score reductions of 0.9-1.3 BLEU points.


This is consistent with the prior findings that the lower layers tend to learn more word-and phrase-level properties than the higher layers, while the top layers of SANs seek more global information (Peters et al., 2018;Yang et al., 2018;Devlin et al., 2019).

we also find that other function words (e.g, "ADP", "NUM","SYM") pay more attention on neighboring signals.


By default, the hidden states of each word are hierarchically calculated by attending to all words in the sentence, which assembles global information.

our hybrid model not only utilizes the local context but also exploits the global information.


In this paper, we propose a sentencelevel agreement module to directly minimize the difference between the representation of source and target sentence.

this relationship has only been represented using the entire neural network and the training objective is computed in wordlevel.


In supervised NMT settings, combining multiple languages to jointly train an NMT system has been found to be successful in improving the performance (Dong et al., 2015;Firat et al., 2016;Johnson et al., 2017).

from Table 1, we observe that proposed multilingual model outperforms the separately trained bilingual models for all translation directions on both test sets with a maximum improvement of 1.48 BLEU points for for Spanish to English on newstest2013.


During training, base Transformer performs about 0.714 steps per second while LPE + LSA model can process around 0.328.

to show the complementary of these two methods, we also placed all edges of lattice in a single sequence in a relative right order based on their first character and use normal positional encodings and our LSA method; we obtained a BLEU of 40.90 which is 0.13 higher than single LSA model.


More strikingly, in many cases, it can match or outperform state-of-the-art models that have access to strong cross-lingual supervision (e.g.

bi-PV  extracts bilingual paragraph vector by sharing the representation between parallel documents.


In Algo-rithm 1, L C , L D and L g are the loss functions for the predictor C, the language discriminator D, and the expert gates in F p and C, respectively.

in addition, note that D is trained using unlabeled corpora over all languages (∆), while the training of F p and C (and hence G) only take place on source languages (S).


For German, however, all nouns are capitalized, and the capitalization features learned on the other three languages would lead to poor results.

in this paper, we concentrate on the more challenging unsupervised CLTL setting, where no target language labeled data is used for training.


Recent advances in deep learning enabled a wide variety of NLP models to achieve impressive performance, thanks in part to the availability of large-scale annotated datasets.

we adopt a MoE module in C, which provides more flexibility than using a single MLP 3 .


These methods aim to find a linear transformation based on the assumption that monolingual word embeddings are approximately isomorphic between languages.

both supervised and unsupervised methods aim to find such a linear transformation that maps word embeddings in a source language into a target language space.


All of the parameters of our model including word embeddings were uniformly initialised in [-0.1, 0.1], and gradient clipping (Pascanu et al., 2013) was used with the clipping value 5.0.

the last condition is hard to assume when dealing with resource-poor languages, for which unsupervised methods can be beneficial in reality.


In Section 1, we provided a (non-exhaustive) list of examples that employ cross-lingual transfer across several tasks.

the implementation is based on the NCRF++ toolkit (Yang and Zhang, 2018).


For POS tagging, the two most important features are dataset size and the TTR distance.

one of our research goals is to understand what linguistic or statistical features of a dataset play important roles in transfer learning, so the interpretable nature of the treebased model can provide valuable insights, which we elaborate further in §6.2.


The results in Table 1 report the average NDCG@3 across all crossvalidation folds.

we turn to formulating our goal as a ranking task: given an NLP task, a low-resource task language t, and a list of J available high-resource transfer languages a 1 , a 2 , .


(2017) study the effect of data selection of neural machine translation, as well as propose a dynamic method to select relevant training data that improves translation performance.

these methods work by extracting features from the pair of languages t i , a j : and then using these features to predict a relative score for each pair of task and transfer languages where θ are the parameters of the ranking model.


Orthographic similarity is computed using a string similarity metric LCSSim based on the longest common subsequence (LCS) of the two input words, returning a similarity score between 0 and 1: When w 1 and w 2 belong to different writing systems, LCS returns 0 and thus the formula above is not directly usable.

the use of Wiktra with respect to existing tools is justified by a need for high-quality results that also cover complex cases of orthography, e.g., in Semitic scripts where vowels are typically omitted.


The use of WikTra with respect to existing tools is justified by a need for high-quality results that also cover complex cases of orthography, e.g., in Semitic scripts where vowels are typically omitted.

in order to cross-check the quality of the output, we randomly sampled 400 cognate pairs not covered by the evaluation corpus and had them re-evaluated by the same experts.


In this paper, we have demonstrated a general method for building a cognate database using existing wordnet resources.

working with cognate densities allows us to characterise the 'cognate content' of each language independently of the wordnet size.


This presents a promising approach to boost performance by utilizing relevance labels from other language pairs.

the former builds representations of query and documents independently, and the matching is performed at the final stage.


They learn a scoring function directly from the relevance label of query-document pairs.

as shown in Figure 1, our model first translates queries and documents and then uses four components to match them in both the source and target language.


First, all word vectors should have the same, constant length.

our analysis leads to Iterative Normalization, an alternating projection algorithm that normalizes any word embedding to provably satisfy both conditions.


For example, English and Japanese fastText vectors (Bojanowski et al., 2017) have different substructures around "girl" (Figure 1 left).

first, they motivate the zero-mean condition based on the heuristic argument that two randomly selected word types should not be semantically similar (or dissimilar) in expectation.


Suppose two embedding matrices X and Z can be aligned with an orthogonal matrix W such that Z = WX.

(2018) optimize CSLS scores between translation pairs instead of Equation (1).


Cao and Zhao (2018) formulate this problem as point set registration to adopt a point set registration method.

if the model can retrieve the correct translation for each word in a source sentence, the translated sentence is more likely to be fluent and grammatically correct.


In this paper, we propose a new method for this task based on multilingual sentence embeddings.

9 We use the ratio margin function with maximum score retrieval for our method.


For languages uncovered by Polyglot, we use its built-in language identifier to select the closest fit.

we also remove all sentences under 3 and over 100 tokens.


These results motivate our proposed approach, which aims to close this gap by directly adapting to the target language through unsupervised learning over unlabeled text.

z represents latent syntax variables that are not observed during training.


We tune hyper-parameters such as vector dimensionality, learning rate and batch size for all models.

zipf's law applies to most languages including Chinese.


The simplest such models obtain word vectors by constructing a low-rank approximation of a matrix containing a co-occurrence statistic (Landauer and Dumais, 1997;Rohde et al., 2006).

we can translate coplanarity in the embedding space to the coplanarity of M's row vectors.


The last row group includes the results of supervised models that utilize training annotations in each specific dataset.

contextual meaning reflects the contexts where the names appear, and it is specifically granted to each 1 Biomedical names refer to surface forms that represent biomedical concepts.


FastText (Bojanowski et al., 2017) leverages this idea by considering character n-grams instead of words.

the encoded representations are constrained by three objectives, namely synonym, context, and concept-based objectives.


Specifically, the encoder will be trained to act like a hash function, which performs well on determining whether two names are synonym of each other.

the selected CUI is then mapped to its associated MeSH or OMIM ID in the CtD dictionary for evaluation.


We propose a novel encoding framework that considers all these aspects in the representation learning.


We introduce a new token type for concepts.

similar to skip-gram baselines, BNE faces serious challenges if the names are unpopular and contain words that do not reflect their conceptual meanings.


These results suggest that attributional information is encoded well in our relational word embeddings.

interestingly, even though not explicitly encoded in our model, the table shows some examples that highlight one property that arises often, which is the ability of our model to capture cohyponyms as relations, e.g., wrist-knee and angerdespair as nearest neighbours of "shoulder-ankle" and "shock-grief", respectively.


Word vector averaging has moreover proven surprisingly effective for learning relation vectors (Weston et al., 2013;Hashimoto et al., 2015;Fan et al., 2015; Espinosa Anke and Schockaert, 2018), as well as in related tasks such as sentence embedding (Wieting et al., 2016).

as is customary in the literature, we compute Pearson correlation with respect to these predefined semantic properties, and measure how well a given set of word vectors is able to predict them, with higher being better.


This trend can easily be observed as well in the philology and assassination cases.

the aforementioned methods have thus far only been evaluated in settings that rely on the predicted relation vectors.


According to the parasiamese loss function we have that, This implies that F θ * (w 1 ) = F θ * (w 3 ), suggesting to F the role of a siamese network.

we also report results with and without pre-training to show the performance gain that pre-training contributes.


If ν = 0 then the original loss function is recovered.

synonymy and antonymy are modeled as relations and a set of axioms is proposed.


In this section, we describe the word embedding results, where we directly compare our model with the following baselines: GloVe (Pennington et al., 2014), Skipgram (Mikolov et al., 2013b) (denoted as SG), Continuous Bag of Words (Mikolov et al., 2013b) (denoted as CBOW), and the recently proposed WeMAP model (Jameel et al., 2019).

our method is clearly different from these works, as our focus is on learning standard word vectors (as well as document vectors).


Next we describe our document retrieval experiments.

to this end, we extend the GloVe model by imposing a prior on the context word vectors.


As evaluation tasks, we use standard word analogy and similarity benchmarks.

to our method, these models are based on topic models (e.g.


The semantics learned in a word can be represented by its shift of position in the embedding space (which is the delta embedding).

we use the ESIM model, a strong baseline in (williams et al., 2018).


the shortest path distance or distance measures that take information beyond the graph structure into account.

experimental Setting To showcase how our approach can be be used inside a graph-based algorithm, we employ word sense disambiguation (WSD) task, reproducing the approach of (Sinha and Mihalcea, 2007).


Additionally, outputs from models with grounded approaches have been observed to be more factually consistent and logically sound (Bordes et al., 2014) compared with outputs from models without grounding information.

this rudimentary approach often detriments the performance of downstream tasks which contain numerous rare or un-seen words.


To modify this method for the hypernym discovery task, we simply sort all vocabulary terms that occur in Hearst Patterns according to the following metric: where U , Σ, and V are taken from the singular value decomposition of the Hearst Pattern cooccurrence matrix and U x , V y are the row vector and the column vector for the hyponym and the hypernym respectively.

this approach is motivated by the work of Snow et al.


We studied the impact of utilizing a hybrid of pattern-based and distributional models for hypernym discovery by hybridizing simple models from each paradigm.

we propose the following ordering rule for candidate hypernyms.


To avoid such cases, we propose to evaluate a candidate's fitness by comparing the sentence's contextualized representation before and after the substitution for validation.

bERT (Keep/Mask) are the baselines that uses bERT unmasking/masking the target word to propose candidates and rank by the proposal scores.


To validate our approach we employ crowd-workers to augment with highlights a recently proposed dataset and compare two state-of-the-art systems.

our evaluation framework comprises three main components: document highlight annotation, highlight-based content evaluation, and clarity and fluency evaluation.


As can be seen, our model outperforms MT-based models on Fluency, Simplicity, and Average overall ratings.

the specific transformation process is: where e 1 (•) and e 2 (•) are both look-up tables.


Although achieving state-of-the-art performances, none of the above work considers the paraphrase patterns at different levels of granularity.

to bridge the gap between training and testing of the transformer models in different domain, we finetune the DNPG model on the sentential paraphrase patterns extracted in source domain.


Table 4 shows the performances of the models, indicating that DNPG achieves competitive performance in terms of all the automatic metrics among all the models.

we can see that the sentence-level paraphrases are more general and abstractive, while the word/phrase-level paraphrases are relatively diverse and domain-specific.


For the sentence-level Transformer, in order to make the positional encoding insensitive to the lengths of the phraselevel fragment, we set: Multi-head Attention: We modify the selfattention mechanism in the encoders and decoders by setting a different receptive field for each granularity, as illustrated in Figure 2.

the m-encoder and m-decoder are composed of multiple independent encoders and decoders, with each corresponding to a specific level of granularity.


For a fair comparison, we use the Transformer model with similar number of parameters with our model.

how can I paraphrase my essay and avoid plagiarism?


The whole generation and alignment process is detailed in Appendix A.

again, determine whether the paraphrase is equivalent to the original question from the perspective of question owner.


The first uses a set of syntax-based hand-crafted transformation rules to perform structural simplification operations, while the second exploits machine learning (ML) techniques where the model learns simplification rewrites automatically from examples of aligned complex source and simplified target sentences.

5 Subtask 3: Rhetorical Relation Identification.


In addition, we assessed the performance of our simplification system using the 5000 test sentences from the WikiSplit benchmark (Botha et al., 2018), which was mined from Wikipedia edit histories.

the former are of equal status and typically depict the key information contained in the input.


1 The goal of our approach is to generate an intermediate representation that presents a simple and more regular structure which is easier to process for downstream semantic applications and may support a faster generalization in ML tasks.

two notable exceptions have to be mentioned.


since most models we tested are in theory capable of handling HANS's examples but failed to do so when trained on MNLI, it is likely that performance could also be improved by training the same architectures on a dataset in which these heuristics are less successful.


Candidate generation (Recall@64) is poor in the Low Overlap category.

our goal is to build entity linking systems that can generalize to new domains and entity dictionaries, which we term worlds.


It is used in the training of the Tree-LSTM model (Choi et al., 2018), as well as policy refinement in our imitation learning.


The PRPN computes the difference between the distance d t at the current step and all previous steps d j for 2 ≤ j < t. The difference is normalized to the range [0, 1]: where τ is the temperature.

if   Predict next word Gated-weighted attention Figure 3: The prediction of the next word in the PRPN language model.


To address this, we annotate the Wall Street Journal part of the Penn Treebank with the gender information of the articles' authors, and build taggers and parsers trained on this data that show performance differences in text written by men and women.

it contains articles published in the WSJ in 1989, as well as a small sample of ATiS-3 material, totalling over one million tokens, and manually annotated with POS tags and syntactic parse trees.


This setting improves performance by relative error reductions of (1.46%, 1.72%) (ACC, LAS) when compared to training on female-only data, and (10.82%, 2.01%) (ACC, LAS) when compared to training on male-only data.

existing data sets fail to meet both criteria: data sets with gender information are either too small to train on, lack syntactic information, or are restricted to social media; sufficiently large syntactic data sets are not labeled with gender information and rely (at least in part) on news genre corpora such as the Wall Street Journal (WSJ).


Nevertheless, this straightforward approach also faces scalability challenges because it requires training an independent parser for each language, which results in over 1.8 billion parameters for ten languages.

we hypothesize that the gains/losses in accuracy for different languages stem from two competing effects: the multilingual model has access to more data, but there are now multiple objective functions competing over the same parameters.


We use the same hyperparameters as in monolingual training but increase the batch size to 256 to account for the increase in the number of languages, and we use a = 0.7 as in Devlin et al.

the obvious approach to parsing ten languages is to fine-tune ten times, producing ten variants of the parameter-heavy BERT layers.


We conduct separate experiments for Bokmål and Nynorsk, which are not distinguished by GT.

we proposed a universal approach for sentiment lexicon induction.


shown in Algorithm 1, with t removed for clarity.

we perform a reverse translation of each candidate back to the source language using the Google translation API to obtains.


This indicates that the transition matrix is a good way to model label dependencies.

we conduct experiments on two datasets, and the experimental results demonstrate that our proposed method achieves comparable results compared with existing methods.


This implies that joint learning is a new direction for ATE task.

both the encoder and decoder have two layers of GRU, and their hidden size is set to 300.


In contrast, the extractive model copies sentences from humanwritten text.

there is a large difference between the extractive oracle ROUGE using our support document and the oracle on full  web sources.


We train several models based on the Transformer architecture (Vaswani et al., 2017), both in its language model and sequence-to-sequence (Seq2Seq) con-   figurations.

to previous datasets where the human written answer could be found with lexical overlap methods (Weissenborn et al., 2017), ELI5 poses a significant challenge in siphoning out important information, as no single sentence or phrase contains the full answer.


In addition, ELI5 contains a diverse set of questions which can involve more than one Wikipedia concept.

the extractive model copies sentences from humanwritten text.


We resize all of the 2, 000 human annotations in VQA-X dataset to 14⇥14 and adjust the object bounding boxes in the images accordingly.

experimental results on the VQA v2 challenge demonstrates that our approach obtains state-of-the-art VQA performance (e.g.


Visualizations of object-level attention show a clear improvement in the ability of the model to attend to small details in complicated scenes.

, where x G c is the GloVe embedding of c, and v b is extracted with the same Faster R-CNN model as described in section 2.1.


In order to identify related objects more precisely, this paper proposes a multi-grained attention mechanism that involves object-level grounding complementary to the sentence-image association.

besides, a more sophisticated language model is adopted for better representation of the question.


Human evaluation with GT captions also shows better performance than with GenP captions as seen in Table 4.

if you are stuck at a certain step and you are given a textual manual which more explicitly describes the names and shapes of the related parts, you could complete that step by reading this additional material and also by comparing it to the visual counterpart.


This strikes the right balance between capturing monolingual and bilingual characteristics.


We compare our proposed methods with the following related works: • Direct bridging (Kuang et al., 2018): this method minimizes the word embedding loss between the transformations of the target words and their aligned source words by adding an auxiliary objective function.

we decide to implement all of our experiments based on Transformer architecture.


The status of events in literature thus inevitably engages larger questions about scale and narrative technique.

since mental states play an especially prominent and complex role in many novels (and noticeably so relative to more fact-based discourses such as the news) we have given particular attention to defining rules for stative events.


RNN-based models) will not learn to be robust to errors since they are never observed (Sperber et al., 2017;Cheng et al., 2018).

we attribute this to the fact that models (e.g.


Yet, the lived reality on social media platforms falls far short of this potential (Papacharissi, 2004).

first, the capabilities approach to social justice focuses on what actions people can do within a social setting (Sen, 2011;Nussbaum, 2003) and provides a useful framework for thinking about what justice online could look like.


Crowdworkers were told to "Talk as you would to a person you have just met."

the results were negative-even with a training set of over 34k examples, the maximum precision we were able to achieve while maintaining at least 10% recall was 0.70, which is insufficient to improve performance on the DI-ALOGUE task.


We applied the stochastic gradient descent (SGD) (Robbins and Monro, 1985) algorithm with mini-batch for optimization.

we used one layer of bidirectional LSTM for encoder and another uni-directional LSTM for decoder, with the size of LSTM hidden state set as 256 in both the encoder and decoder.


Given dialogue context C t in the t-th turn, we first embed each utterance in C t using a Gated Recurrent Unit (GRU) (Chung et al., 2014) based bidirectional recurrent neural networks (bi-RNNs).

in summary, our main contributions are threefold: (1) To the best of our knowledge, this is the first work to study the incremental learning framework for task-oriented dialogue systems.


In this paradigm, all reasonable queries related to the current task are legal, and the system can learn to deal with user queries online.

due to the uncertainty estimation module, IdS − and IdS will refuse to respond if there is low confidence.


Because IDS requires human intervention to reduce risks whenever there is low confidence, we calculate the average turn accuracy only if IDS chooses to respond without human intervention.

(2018) have reserved a few bits in the dialogue state for the domain extension.


c k is the cell at time k, and σ denotes the sigmoid function.

in the encoding phase of these HRED  models, a recurrent neural network (RNN) based encoder is first utilized to encode each input a context to a vector, and then a hierarchical RNN is conducted to encode these vectors to one vector.


these results indicate that the relevant contexts detected by our ReCoSa model are highly coherent with human's judgments.


Furthermore, our further analysis show that the relevant contexts detected by our model are significantly coherent with humans' judgements.

in this paper, we propose a new model, namely ReCoSa, to tackle this problem.


, s N } and the generated response } as the response representation to obtain the t th word distribution in the generation response.

the cosine similarity is conducted between each context and the post, with the assumption that the relevance between a context and a response is equivalent to the relevance between the context and the corresponding post, which is clearly insufficient in many cases, e.g.


The problem is to learn a function f NLI (s 1 , s 2 ) → {E, N, C} which generalizes to new input pairs.

the dataset offers a new domain for natural language inference models, and suggests avenues such as devising alternative methods for using natural language inference components in downstream tasks.


s j is a sequence of words (w 1 , ..., w K ) where each word w k is from a vocabulary V. The input (s 1 , s 2 ) are referred to as the premise and hypothesis, respectively, and each label is interpreted as meaning the premise entails the hypothesis, the premise is neutral with respect to the hypothesis, or the premise contradicts the hypothesis.

a contradiction score is computed for each candidate as: if the candidate u i does not contradict any persona sentence p j according to the NLI  is the highest confidence, c i,j , out of the contradicting (u i , p j ).


Cluster5 BS also has the highest SPICE@10 score, indicating it is the best method for generating at least one high quality candidate.

we take the negative of the Hamming distance as θ. Iterative Beam Search In an attempt to improve the size of the search space explored without sacrificing runtime, Kulikov et al.


We take the negative of the Hamming distance as θ. Iterative Beam Search In an attempt to improve the size of the search space explored without sacrificing runtime, Kulikov et al.

standard beam search is not ideal for producing diverse outputs.


Note that Rtr has the highest informativeness mean score due to its diverse human-written content.

besides, most responses in Rtr are annotated as +2 or 0 in informativeness.


(2019) augments the decoder with an edit vector representing lexical differences between retrieved contexts and the message.

its objective function is to minimize classification error rate: We cast the retrieval-enhanced response generation as a reinforcement learning problem to backpropagate the error computed by the discriminator to the generator via the policy gradient algorithm.


Curriculum learning is a learning strategy in machine learning, where the key idea is to start easier aspects of the targeted task and then gradually increase the complexity.

differently, we propose multi-pass encoding and decoding with multi-level vocabularies to deal with the above two problems simultaneously.


The total losses are minimized by an Adam optimizer (Kingma and Ba, 2015) with 0.0001 learning rate.

in addition to automatic evaluations, we also leverage manual evaluations to enhance the evaluations.


1) w/o low-level ED: without lowlevel encoder and low-level decoder; 2) w/o high-level ED: without high-level encoder and highlevel decoder; 3) w/o low&high-level ED: without low-level encoder/decoder and high-level encoder/decoder, which is the same as the Seq2Seq model with attention mechanisms.

vPN incorporates multipass encoding and decoding with multi-level vocabularies into response generation.


Overall complexity for inference with the SGNN++ model depends on the projection layer, O(n • T • d) where n is the observed feature size (*not* overall vocabulary size) which is linear in input size, d is the number of LSH bits specified for each projection vector P j , and T is the number of projection functions used.

it is easy to customize our model for different devices such as watches, phones or ioT with different size constraints.


Furthermore, we also evaluate the ability of exploiting knowledge of each model by calculating knowledge precision/recall/F1 scores.

• score 1 (fair): there are some errors in the response text but still can be understood.


This somehow indicates the gap between training and test in real applications for the existing research on response selection, and thus demonstrates the merits of this work.

the average number of positive responses per context is 1.18.


DAM:  performs matching under a representation-matching-aggregation framework, and represents a context and a response with stacked self-attention and crossattention.

one model may guide the other how to pick high quality training examples and circumvent noise; second, since the peer models are optimized with different training sub-batches, knowledge from one sub-batch could be supplementary to the other through exchange of learning protocols; third, the two peer models may have different decision boundaries, and thus are good at recognizing different patterns in data.


Experimental results illustrate the superior performance of the proposed model in generating diverse and appropriate responses compared to previous representative approaches.

experimental results show that our method can outperform existing competitive neural models under both automatic and human evaluation metrics, which demonstrates the effectiveness of the overall approach.


The second generation phase aims to model each specific response in a response bag respectively.

it is observed that in the CVAE with a fixed Gaussian prior, the learned conditional posteriors tend to collapse to a single mode, resulting in a relatively simple scope (Wang et al., 2017).


− log p(u gt |u 1 , ...q, ...u T ), where u gt is the original utterance at index t. The final loss function is: = − log exp(α gt ) MUR is analogous to the MLM objective of Devlin et al.

it is still unclear whether language modeling is the most effective method of pretrained language representation, especially for tasks that need to exploit multi-turn dependencies, e.g.


Also, the 18,924 training message set initially only had 100 messages of context per sample, and we later added another 900 lines and checked every message that was not a reply to see if it was a response to something in the additional context.

beyond this difference it is hard to identify consistent variations in the annotations.


Next, as described in Section 2.3, we report Spearman's rank correlation (ρ) of the model's probabilities for the various nonce past tense forms with the human production probabilities.

cR@5 also yielded unstable results: 39-47% on A&H's data, and 29-44% on K&c's data, as shown in Figure 3.


We track the cumulative activation (CA) of the mentioned concepts at different points in time: at encounter (AE), at the end of sentences (AEoS) and at the end of paragraphs (AEoP).

then at pulse 1, the seed fires and the SA process starts.


We adopt a batch size of 2, 2 × 256 dimension hidden state BiLSTM, SGD optimiser, and weighted cross entropy loss where y i is a ground truth label for a word at position i.ŷ i is its prediction.

the BiL-STM hidden state h t is The intuition behind SPV is that metaphoricity is identified by detecting the incongruity between a target word and its context.


Finally, we try to interpret the semantics of the agents' messages.

we want to assess agent B's behaviour under other possible received messages m A t .


This impressionistic observation is supported by the fact that, for both splits, the narrow-kernel errors have considerably larger variance than the wide-kernel errors with respect to ground-truth length, indicating that, with narrow decoder kernel, the model is less stable in terms of output sequence length.

indeed, with the narrow decoder we obtain around-right accuracies that are even above the absolute-best jump-split performance.


2 shows a big difference in stability between random and the other splits across top hyperparameter configurations.

it is hard to train very deep LSTMs, and it is not clear that the latter models need the same depth CNNs require to "view" long sequences.


Note that we call the vector π due to a spiritual similarity to the principle-and-parameters framework of Chomsky (1981).

what we would like to know is how often p(π i | π j ) is significantly different than p(π i ).


Indeed, logical implications are a special case of conditional probabilities that only take the values 0 and 1, rather than values in [0, 1].

we draw inspiration from manual linguistic efforts to this problem (Greenberg, 1963;Lehmann, 1978), as well as from previous computational methods (Daumé III and Campbell, 2007;Bjerva et al., 2019a).


For lack of space, we will only briefly describe the algorithms drawn from WordSeg (see Johnson and Goldwater 2009;Monaghan and Christiansen 2010;Lignos 2012;Daland and Zuraw 2013;Saksida et al.

our results support previous experimental findings that infants may use such tools to acquire language.


Parsing Each corpus was tokenized using UDPipe's (Straka et al., 2016) pre-trained UDv2.2 models (Straka, 2018) and then parsed as follows: We trained Dozat et al.

corpus studies of English, Pirahã, and a few other-mostly European-languages proposed constraints at one (Reich, 1969;Futrell et al., 2016), two (De Roeck et al., 1982), or three (Karlsson, 2010) levels of embedding.


These suggest a near-equal distribution of feminine and masculine target pronouns (he/him/his vs. she/her) as well as an equal distribution of the two labels, which keeps chance-based performance at 50% expected accuracy.

with respect to our setting, several errors can be made by these existing models: predicting that the two entities and the pronoun share a similar cluster (Both Antecedents Predicted), that none of the two candidates shares a cluster with the pronoun (No Decision), or creating a cluster that contains the pronoun with the wrong candidate (Incorrect Decision).


The use of sentence function has shown promising results to improve the performance of conversation models.

a discriminator is added to constrain that the encoding information from z can well realize its corresponding sentence function d y .


Our next experiment exploits this benefit.

second, holistic scoring technologies are commercially valuable: being able to successfully automate the scoring of the millions of essays written for aptitude tests such as sAT, GRE, and GMAT every year can save a lot of manual grading effort.


Our modeling experiment shows that the straightforward application of machine translation for model transfer to another language does not lead to a drop in prediction performance.

to ISEAR, we account for the fact that a description can be re-lated to different emotions by its writer and its readers.


The Story Cloze Test and ROCStories Corpora (Mostafazadeh et al., 2016;Sharma et al., 2018) require to figure out the right ending from two candidate sentences after a foursentence story.

it is also important to note that human labelled commonsense knowledge bases are in-evitably limited by the scope.


It also has the sentences describing the corresponding triples.

we hope this benchmark can promote commonsense reasoning by the NLP community, and further applied on other applications such as machine translation and dialogue.


Our dataset is the first benchmark for direct linguistic sense making and explanation.

for Sen-Making, ELMo does better than BERT; BERT beats ELMo in Explanation.


As expected, more data significantly improve classification quality on FUN test set in case of both methods.

manual annotation of 1,877 examples confirmed the validity of the automatic approach and formed a golden test set.


The task of humor recognition has attracted a lot of attention recently due to the urge to process large amounts of user-generated texts and rise of conversational agents.

lM-based approach can be seen as indirect reflection of some common humor features such as incongruity, unexpectedness, or nonsense.


To do so we trained 6 UG-WGAN models on the following languages: English, Russian, Arabic, Chinese, German, Spanish, French.

this is a future direction that can be explored.


We take inspiration from the UG hypothesis and learn latent representations that are language agnostic which allow us to solve downstream problems in new languages without the need of any language-specific training data.

we can utilize an arbitrary set of languages in training an auxiliary task over UG encodings.


(3) to have: Given the probability density functions of Dirichlet and categorical distributions, Eq.

leveraging hierarchical meta-info: MIGA can be extended to accommodate hierarchical metainfo (e.g., an academic paper labelled with tags "computer science→machine learning→deep learning").


It can be observed that the documents in Web Snippets labelled with "Computers" are quite diverse, which can be further clustered into the ones related to "hardware", "digital products", "programming language", and so on.

7 The scores of KMeans + TFIDF on Tweets are not reported because it exceeds the memory of our machine.


Furthermore, by comparing with prior work, we argue that learning from unlabelled corpora indeed helps to improve the sentence representations, although the current way of utilising corpora might not be optimal.

in one of the two classes of models of this type, an encoder-decoder model is learnt using a corpus of contiguous sentences Gan et al., 2017;Tang et al., 2018) to make predictions of the words in the next sentence given the words in the current one.


Future work could potentially expand our work into an end-to-end invertible model that is able to produce high-quality representations by omnidirectional computations.

it is important to check the necessity of the invertible constraint on the decoders.


To allow the learning system to explore more powerful transformation, we follow the design of the 'affine coupling layer' (Dinh et al., 2016): where s : are both neural networks with linear output units, and • is the Hadamard product.

current ways of incorporating the distributional hypothesis only utilise it as a weak and noisy supervision, which might limit the quality of the learnt sentence representations.


Other works examine deep AD on general high-dimensional point data (Sakurada and Yairi, 2014;Xu et al., 2015;Erfani et al., 2016;Chen et al., 2017).


Using X and Y, a linear mapping T can be learned, which "aligns" the X and Y space.

bLI is the task of creating a lexicon of translation equivalents such as, bank:banc or bank:banque automatically from non-parallel corpora.


Given the distance matrix D, ISF seeks a matrix ISF where its i, j-th entry decides the probability of translating the i-th source word to the j-th target.

it is still not clear why ISF works so well.


MIL-ND significantly outperforms MIL: the 95% confidence intervals for them do not overlap.

the oracle recall for surface name matching (i.e.


Training Our goal is to down-weight potentially noisy data points.

little progress has been made for the settings where no or very limited amounts of labeled data are present (e.g., legal or most scientific domains).


To address this mismatch challenge, we introduce a new approach that learns an AL query strategy directly for the target problem of interest.

to improve the policy, we use the trained student learner as an imperfect annotator in order to improve the policy via simulations using data of the AL task of interest.


The results show that directly fine-tuning on target corpus can lead to extremely bad performance, due to the insufficiency of data.

furthermore, we conduct an ablation study to analyze the effect of morphological features.


Recent approaches for NER take word embedding as input and leverage its semantic information to annotate named entities.

the nature of metric learning makes it specified on classification problems.


In another work (Khodak et al., 2018), a simple linear function is used to infer embedding of an OOV word by aggregating embeddings of its context words in the examples.

our method (HiCE+Morph+MAML) 6 achieves the best performance among all the other baseline methods under most settings.


To capture the semantic shifts across time domains, our intuition is to model the dynamic process.

the first T − 1 time domains are used for training; the last time domain is split into two equal-sized sets for development and testing.


In our network, both the source specific part and the common part contribute positively to the source classification objective (i.e., minimize the mis-classification loss).

the comments were pre-processed by converting it to lowercase followed by stemming.


The comparison could be described as follows: For two NLP models on a given text corpus, which model produces a higher performance system with a relatively high probability?

al., 2015) IOBES (this study) IOBES (Wang et.


The probabilities of the hypotheses and decisions are given in Table 3.

moreover, the fine-grained tag set, "IOBES," exerts more effort to improve P than R because P (H 1 ) = 0.68 for P is larger than P (H 1 ) = 0.63 for R.  4.3 ORG Task: "IOB2" Versus "IOBES" In this task, the settings of features are the same with the NER task.


Table 4 presents the decisions of the Bayes test, which are identical on the three metrics, that is, "Accept H 1 ".

for NLP practitioners, we recommend here three guidelines: (1) A t-test should be avoided in a comparison of two NLP models on the basis of the precision, recall and f 1 measure.


Furthermore, since the number of overlapping samples between the two training sets in P j and P j ′ equals to n/4 with j ̸ = j ′ , we could reasonably assume Cov for all j ̸ = j ′ are identical and independent to j and j ′ .


A different CI of F 1 based on 3 × 2 BCV is given in (Wang et al., 2015), which employs the averaged values of FPs, FNs, and TPs in M. Their CI is a special case of Eq.

the DOC and IL of our CI are 94.5% and 0.0854.


for damaged or historical documents) (Berglund et al., 2015), acrostic poetry generation (Liu et al., 2018a), and text representation learning (Devlin et al., 2018).

the whole algorithm is further summarized in 1.


Seq2Seq-f struggle to reason about word transitions on the forward side of the blank, so the words filled in by Seq2Seq-f usually abruptly clash with existing words behind the blank.

such methods are only used for unconditional text infilling tasks, whereas many text infilling tasks are conditional, e.g., conversation reply with templates.


(2017b), this momentum based optimizer can stabilize update directions and escape from poor local maxima during the iterations for adversarial attack.

given a well-trained sequential generative model, generating missing symbols conditioned on the context is challenging for existing greedy approximate inference algorithms.


The lower the entropy, the sharper the attention distribution.

focusing the attention on the relevant parts of the input.


(2015) has the smallest difference between its maximum and minimum span evaluation scores.

the use of minimum spans in coreference evaluation decreases the effect of mention boundary detection errors in coreference evaluation and results in fairer comparisons.


Differently from Choubey and Huang (2017), they do not cluster documents to topics as a pre-processing step.

our model achieves state-of-the-art results, outperforming previous models by 10.5 CoNLL F 1 points on events, and providing the first cross-document entity coreference results on ECB+.


Pointer Networks (Vinyals et al., 2015) address this limitation by using attention (Bahdanau et al., 2015) as a pointing mechanism.

we conduct a series of experiments with our framework on the standard RST Discourse Treebank (RST-DT) dataset for sentence level parsing, and our main findings are as follows.


We propose several techniques to enable such knowledge transfer that yield the state-of-the-art performance for IDRR on several settings of the benchmark dataset (i.e., the Penn Discourse Treebank dataset).

in order to exploit the connective-relation mapping, we propose to embed the implicit connectives and the discourse relations into the same space that would be used to transfer the knowledge between connective and relation predictions via the mapping.


Our main observation in this paper is that while models for learning script knowledge improved significantly over the last decade, these models can essentially represent only a single event relationship, co-occurrence.

we also make two variants to show our models' awareness to relation types.


We combine EventTransE with the ELMo baseline by having another attention layer on top of the event embeddings and concatenating all the argument  representations in the network.

events appearing in similar contexts tend to have similar representations.


This implies that the distance between {r org i } and {r out i } might be much larger than that between {r org i } and {r in i } if F RV focused equally on every answer passage word.

like the original GAN, an AGR is composed of a generator and a discriminator: the generator network is trained for generating (from answer passages) fake representations to make it hard for the discriminator network to distinguish these fake representations from the true representations derived from manually created compact-answers.


In this work, we propose a data augmentation technique by automatically generating relevant unanswerable questions according to an answerable question paired with its corresponding paragraph that contains the answer.

both answers and plausible answers are text spans in the paragraphs.


Finally, the operations Which is true and Intersection are single-hop because they can be answered using one paragraph regardless of the context.

", and then answer "What is the former name of that animal?".


bridge questions consist of two paragraphs linked by an entity (Yang et al., 2018), e.g., Figure 1.


We report the F1 score of single-paragraph BERT on these new distractors in Table 4: the accuracy declines from 67.08 F1 to 46.84 F1.

using these extracted This research was supported by ONR (N00014-18-1-2826, N00014-17-S-B001), NSF (IIS-1616112, IIS-1252835, IIS-1562364), ARO (W911NF-16-1-0121), an Allen Distinguished Investigator Award, Samsung GRO and gifts from Allen Institute for AI, Google, and Amazon.